Implementação de um planejador de rotas baseado em ACS para ambientes bidimensionais estáticos utilizando o ROS
Resumo
O presente trabalho descreve a implementação de um planejador de rotas com aplicação de inteligência artificial, de forma a automatizar o processo de definição e execução de uma trajetória em um ambiente bidimensional estático. O planejador de rotas foi aplicado a um robô, utilizando o ambiente de desenvolvimento do Robot Operating System, possibilitando seu processo de navegação em um ambiente com obstáculos. Neste trabalho, são abordados aspectos em relação ao uso do algoritmo Ant Colony System para o planejamento de rotas a partir de um Grafo de Voronoi e à adaptação e implementação deste método no ambiente do ROS.Referências
Anibrika, B. S., Asante, M., Hayfron-Acquah, B., and Ghann, P. (2020). A survey of modern ant colony optimization algorithms for manet: Routing challenges, perpectives and paradigms. International journal of engineering research and technology, 9.
Binder, B., Beck, F., König, F., and Bader, M. (2019). Multi robot route planning (mrrp): Extended spatial-temporal prioritized planning. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 4133–4139.
Bolc, L. and Cytowski, J. (1992). Search Methods for Artificial Intelligence. Academic Press.
Dai, X., Long, S., Zhang, Z., and Gong, D. (2019). Mobile robot path planning based on ant colony algorithm with a* heuristic method. Frontiers in Neurorobotics, 13.
De Matos, J. M. A. and Dias, A. M. (2013). Modelagem e implementação de um sistema off-line de planejamento de rotas para ambientes bidimensionais estáticos.
Dorigo, M., Birattari, M., and Stützle, T. (2006). Ant colony optimization-artificial ants as a computational intelligence technique. IEEE Computational Intelligence Magazine.
Dorigo, M. and Stützle, T. (2004). Ant Colony Optimization. The MIT Press, Cambridge, MA.
Latombe, J. C. (2004). Robot Motion Planning. Kluwer Academic Publishers, USA, 8th edition.
Li, Y., Dong, T., Bikdash, M., and Song, Y.-D. (2005). Path planning for unmanned vehicles using ant colony optimization on a dynamic voronoi diagram. pages 716–721.
ROS.org (2022). Wiki: Documentation. [link].
Sadavare, A. and Kulkarni, R. (2012). A review of application of graph theory for network. International Journal of Computer Science and Information Technologies, 3(6):5296–5300.
Schwartz, J. T. and Sharir, M. (1983). On the piano movers’ problem: Iii. coordinating the motion of several independent bodies: The special case of circular bodies moving amidst polygonal barriers. The International Journal of Robotics Research, 2(3):46–75.
Wu, S., Li, Q., and Wei, W. (2023). Application of ant colony optimization algorithm based on triangle inequality principle and partition method strategy in robot path planning. Axioms, 12(6).
Zhang, D., You, X., Liu, S., and Pan, H. (2020). Dynamic multi-role adaptive collaborative ant colony optimization for robot path planning. IEEE Access, 8:129958–129974.
Binder, B., Beck, F., König, F., and Bader, M. (2019). Multi robot route planning (mrrp): Extended spatial-temporal prioritized planning. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 4133–4139.
Bolc, L. and Cytowski, J. (1992). Search Methods for Artificial Intelligence. Academic Press.
Dai, X., Long, S., Zhang, Z., and Gong, D. (2019). Mobile robot path planning based on ant colony algorithm with a* heuristic method. Frontiers in Neurorobotics, 13.
De Matos, J. M. A. and Dias, A. M. (2013). Modelagem e implementação de um sistema off-line de planejamento de rotas para ambientes bidimensionais estáticos.
Dorigo, M., Birattari, M., and Stützle, T. (2006). Ant colony optimization-artificial ants as a computational intelligence technique. IEEE Computational Intelligence Magazine.
Dorigo, M. and Stützle, T. (2004). Ant Colony Optimization. The MIT Press, Cambridge, MA.
Latombe, J. C. (2004). Robot Motion Planning. Kluwer Academic Publishers, USA, 8th edition.
Li, Y., Dong, T., Bikdash, M., and Song, Y.-D. (2005). Path planning for unmanned vehicles using ant colony optimization on a dynamic voronoi diagram. pages 716–721.
ROS.org (2022). Wiki: Documentation. [link].
Sadavare, A. and Kulkarni, R. (2012). A review of application of graph theory for network. International Journal of Computer Science and Information Technologies, 3(6):5296–5300.
Schwartz, J. T. and Sharir, M. (1983). On the piano movers’ problem: Iii. coordinating the motion of several independent bodies: The special case of circular bodies moving amidst polygonal barriers. The International Journal of Robotics Research, 2(3):46–75.
Wu, S., Li, Q., and Wei, W. (2023). Application of ant colony optimization algorithm based on triangle inequality principle and partition method strategy in robot path planning. Axioms, 12(6).
Zhang, D., You, X., Liu, S., and Pan, H. (2020). Dynamic multi-role adaptive collaborative ant colony optimization for robot path planning. IEEE Access, 8:129958–129974.
Publicado
05/11/2024
Como Citar
ARAÚJO, Esther de S.; DIAS, Anfranserai M..
Implementação de um planejador de rotas baseado em ACS para ambientes bidimensionais estáticos utilizando o ROS. In: ESCOLA REGIONAL DE COMPUTAÇÃO BAHIA, ALAGOAS E SERGIPE (ERBASE), 24. , 2024, Salvador/BA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 94-103.
DOI: https://doi.org/10.5753/erbase.2024.4515.
